Journal Article

Detecting Small-Area Similarities in the Epidemiology of Childhood Acute Lymphoblastic Leukemia and Diabetes Mellitus, Type 1: A Bayesian Approach

Richard G. Feltbower, Samuel O. M. Manda, Mark S. Gilthorpe, Mel F. Greaves, Roger C. Parslow, Sally E. Kinsey, H. Jonathan Bodansky and Patricia A. McKinney

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 161, issue 12, pages 1168-1180
Published in print June 2005 | ISSN: 0002-9262
Published online June 2005 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwi146
Detecting Small-Area Similarities in the Epidemiology of Childhood Acute Lymphoblastic Leukemia and Diabetes Mellitus, Type 1: A Bayesian Approach

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Childhood acute lymphoblastic leukemia and diabetes mellitus, type 1, have common epidemiologic and etiologic features, including correlated international incidence and associations with infections. The authors examined whether the diseases' similar large-scale distributions are reflected in small geographic areas while also examining the influence of sociodemographic characteristics. Details of 299 children (0–14 years) with acute lymphoblastic leukemia and 1,551 children with diabetes diagnosed between 1986 and 1998 were extracted from two registers in Yorkshire, United Kingdom. Standardized incidence ratios across 532 electoral wards were compared using Poisson regression, confirming significant associations between population mixing and the geographic heterogeneity of both conditions. Bayesian methods analysis of spatial correlation between diseases by modeling a bivariate outcome based on their standardized incidence ratios was applied; spatial and heterogeneity components were included within a hierarchical random effects model. A positive correlation between diseases of 0.33 (95% credible interval: −0.20, 0.74) was observed, and this was reduced after control for population mixing (r = 0.18), population density (r = 0.14), and deprivation (r = 0.06). The Bayesian approach showed a modest but nonsignificant joint spatial correlation between diseases, only partially suggesting that the risk of both was associated within some electoral wards. With Bayesian methodology, population mixing remained significantly associated with both diseases. The links between diabetes and acute lymphoblastic leukemia observed for large regions are weaker for small areas. More powerful replications are needed for confirmation of these findings.

Keywords: Bayesian analysis; child; diabetes mellitus, type 1; hierarchical model; infection; leukemia; spatial model

Journal Article.  7095 words.  Illustrated.

Subjects: Public Health and Epidemiology

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